Authors : Matthew Busayo Olanrewaju; Cornelius Chukwujekwu Okafor; Emmanuel Ayodeji Afolabi

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/3z45vpk2

DOI : https://doi.org/10.38124/ijisrt/26sep157

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Reliable electric vehicle (EV) charging infrastructure is increasingly important as charging networks expand. The study developed an explainable machine-learning framework to determine if a charger would document a maintenancerelevant fault in the next three calendar days. A Chinese dataset of 441,077 charging sessions from 92 chargers was transformed into a continuous charger-day panel with 16 operational, environmental, fault-history and rolling-workload predictors. The performance of Logistic Regression, Random Forest, XGBoost and LightGBM was compared via leakagecontrolled temporal validation, operating-threshold selection on the independent validation period, and final evaluation on a later temporal holdout.

Keywords : Electric Vehicle Charging Infrastructure; Predictive Maintenance; Fault Prediction; Explainable Artificial Intelligence; Logistic Regression; Temporal Validation.

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